code: def forward(self, x_dict, edge_index_dict, edge_label_index):
      z_dict = {}
      x_dict["user"] = self.user_emb(x_dict["user"])
      x_dict["item"] = self.item_emb(x_dict["item"])
      z_dict["item"] = self.item_encoder(
          x_dict["item"],
          edge_index_dict[("item", "to", "item")],
      )
      z_dict["user"] = self.user_encoder(x_dict, edge_index_dict)
  
      return self.decoder(
          z_dict["user"], z_dict["item"], edge_label_index
      )
  
imports: import os.path
  import torch
  import torch.nn.functional
  import tqdm
  from sklearn.metrics import roc_auc_score
  from torch.nn import Embedding, Linear
  import torch_geometric.transforms
  from torch_geometric.datasets import Taobao
  from torch_geometric.loader import LinkNeighborLoader
  from torch_geometric.nn import SAGEConv
  from torch_geometric.utils.convert import to_scipy_sparse_matrix
label_desc: Use image node_id 2 for calling the Model obj's underlying member method code with example usage: obj.forward(x_dict, edge_index_dict, edge_label_index) and returns: self